The spatial-temporal distributions of controlling factors on vegetation growth in Tibet Autonomous Region, Southwestern China
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Bibliographic record
Abstract
Abstract Due to cold and arid climate of Tibet Autonomous Region, vegetation growth is considered to be controlled by both moisture availability and warmth. In order to reveal the patterns of regional climate change and the mechanisms of climate-vegetation interactions, long term (1982–2013) datasets of climate variables and vegetation activities were collected from Climatic Research Unit (CRU) and Global Inventory Monitoring and Modeling System (GIMMS). Principal regression analysis and (partial) correlation analysis were conducted to reveal the contributions of controlling factors on vegetation growth. Study results showed that (1) Annual mean air temperature (TMP) had increased by 0.38 °C per decade (P = 0.00) and annual precipitation (PRE) had increased by 17.25 mm per decade (P = 0.15). A significant change point around the year 1997/1998 was detected by Mann-Whitney-Pettit test, coinciding with the occurrence of El Niño event. (2) Normalized Difference Vegetation Index (NDVI) had an insignificant positive trend. Spatially, pixels of high NDVI values, great NDVI trends and high inter-annual deviations are distributed in the densely vegetated eastern part. Principal regression analysis revealed that, alpine grassland (northern and western part) is mostly controlled by temperature, steppe meadow (middle and southern part) is mostly controlled by precipitation, and shrub/mixed needle leaved and broad leaved forest (eastern part) is mostly controlled by cloud coverage. (3) Partial correlation analyses showed that regions with high sensitivity to precipitation nearly overlapped with regions of high sensitivity to minimum temperature. And the high importance of cold index (CDI, accumulated negative difference between TMP and 5 °C) revealed in this study implied the effects of regional glacial melting and permafrost degradation. We concluded that the regional climate change can be characterized as warming and wetting. Different regions and vegetation types in Tibet Autonomous Region demonstrated different driving climate factors and climate-vegetation relationships.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it